{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate(T, sig_0, P_0, del_t):\n",
    "    t = 0\n",
    "    prices = [P_0]\n",
    "    log_price = [np.log(P_0)]\n",
    "    while t < T: \n",
    "        curr_y = log_price[-1]\n",
    "        w = np.random.normal(0, 1, 1)[0]\n",
    "        del_w = w*np.sqrt(del_t)\n",
    "        # get sig_t    \n",
    "        intgr = 0.\n",
    "        curr_T = len(prices)\n",
    "        if t == 0:\n",
    "            sig_t = sig_0\n",
    "        else:  \n",
    "            for tau in range(1, curr_T):\n",
    "                intgr += (g/((sig_0**2) * (tau**lamb))) * ((curr_y - log_price[-tau])**2)\n",
    "            sig_t = sig_0 * np.sqrt(1.+intgr)\n",
    "            print 'sig_t at ', t, 'is: ', sig_t\n",
    "        y_next = sig_t*del_w + curr_y\n",
    "        log_price.append(y_next)\n",
    "        prices.append(np.exp(y_next))\n",
    "        t += 1\n",
    "    return prices, log_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1510, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>27.9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>28.0875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>28.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>28.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>28.6000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ticker       date    close\n",
       "None                           \n",
       "0      MSFT 2011-01-03  27.9800\n",
       "1      MSFT 2011-01-04  28.0875\n",
       "2      MSFT 2011-01-05  28.0000\n",
       "3      MSFT 2011-01-06  28.8200\n",
       "4      MSFT 2011-01-07  28.6000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import quandl\n",
    "quandl.ApiConfig.api_key = \"YsgeJmyZtvDmyVdYinwf\"\n",
    "data = quandl.get_table('WIKI/PRICES', \\\n",
    "                        qopts = { 'columns': ['ticker', 'date', 'close'] }, \\\n",
    "                        ticker = ['MSFT'], \\\n",
    "                        date = { 'gte': '2011-01-01', 'lte': '2016-12-31' })\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ffc0d90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xz93wxlZ9ixR7V2WmqpTMVIPLGxTYRcpRNfM1IiM+4s/uLgymiIiIeoDstDgAQJWteYF1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ffc04d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.mpl.rcParams['figure.figsize'] = (10.0, 6.0)\n",
    "seaborn.mpl.rcParams['savefig.dpi'] = 90\n",
    "data.plot(x='date', y='close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20271350>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D2Pnrctwqb5pzF+KypowmX4LIOVe/MgNlw0fmuxk7NSSUNSEiEkw1X4poTFMupaATyh/f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\nAATg/wHW3vwD6+NfLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e378990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot log_returns\n",
    "price = data\n",
    "# log_return = log(pi/pj)\n",
    "log_pj = list(np.log(price['close']))\n",
    "log_pi = log_pj[1:]\n",
    "log_pi.append(0)\n",
    "assert len(log_pi) == len(log_pj)\n",
    "price['log_ret'] = np.array(log_pi) - np.array(log_pj)\n",
    "price = price[:-1]\n",
    "price.plot(x='date',y = 'log_ret')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig_t at  1 is:  0.015\n",
      "sig_t at  2 is:  0.0163824959121\n",
      "sig_t at  3 is:  0.0156327330175\n",
      "sig_t at  4 is:  0.0154396356139\n",
      "sig_t at  5 is:  0.0272742403502\n",
      "sig_t at  6 is:  0.0427062228606\n",
      "sig_t at  7 is:  0.038623136197\n",
      "sig_t at  8 is:  0.0349801960023\n",
      "sig_t at  9 is:  0.0833454272532\n",
      "sig_t at  10 is:  0.0783603297804\n",
      "sig_t at  11 is:  0.0505573750336\n",
      "sig_t at  12 is:  0.102849277768\n",
      "sig_t at  13 is:  0.0919605345529\n",
      "sig_t at  14 is:  0.101928317131\n",
      "sig_t at  15 is:  0.249697983009\n",
      "sig_t at  16 is:  0.527381624537\n",
      "sig_t at  17 is:  0.806517138856\n",
      "sig_t at  18 is:  0.685662232396\n",
      "sig_t at  19 is:  1.14849530568\n",
      "sig_t at  20 is:  0.608700770272\n",
      "sig_t at  21 is:  0.905919190559\n",
      "sig_t at  22 is:  0.795831910484\n",
      "sig_t at  23 is:  0.655344042752\n",
      "sig_t at  24 is:  0.542879306988\n",
      "sig_t at  25 is:  1.12297573855\n",
      "sig_t at  26 is:  1.39851312213\n",
      "sig_t at  27 is:  1.18293950538\n",
      "sig_t at  28 is:  2.85439277896\n",
      "sig_t at  29 is:  3.94507780488\n",
      "sig_t at  30 is:  5.26359024\n",
      "sig_t at  31 is:  6.10006341458\n",
      "sig_t at  32 is:  3.93142837205\n",
      "sig_t at  33 is:  3.87874376687\n",
      "sig_t at  34 is:  3.24224010725\n",
      "sig_t at  35 is:  2.86157998098\n",
      "sig_t at  36 is:  6.71537887992\n",
      "sig_t at  37 is:  4.66914851301\n",
      "sig_t at  38 is:  7.42033773634\n",
      "sig_t at  39 is:  5.99325223034\n",
      "sig_t at  40 is:  4.58369999206\n",
      "sig_t at  41 is:  4.817037536\n",
      "sig_t at  42 is:  8.70159023152\n",
      "sig_t at  43 is:  23.2092804905\n",
      "sig_t at  44 is:  10.2455784368\n",
      "sig_t at  45 is:  9.53866877591\n",
      "sig_t at  46 is:  9.92654004939\n",
      "sig_t at  47 is:  7.50926594584\n",
      "sig_t at  48 is:  7.94533741606\n",
      "sig_t at  49 is:  9.62685678126\n",
      "[27.98, 27.754238656858274, 27.460420416871649, 27.63030484946777, 27.568866899171233, 26.952545678400799, 26.348730192093512, 26.32241605702735, 26.333280492785097, 24.936003946793399, 24.834162577770741, 25.904076235883327, 28.109133164292306, 25.070559758506228, 28.005819728714588, 32.348815972067342, 41.878477942101171, 16.679991306862714, 16.659421798446136, 10.379945939587925, 17.392544777649338, 36.059334127074088, 36.955053284132902, 34.79287413822621, 32.185602762574256, 59.125489201478928, 80.866671874214461, 72.736014971769279, 299.24782499486923, 3.8940029501698481, 0.85838179936976333, 844.39008846657123, 6.2647563818607868, 3.4334267930323681, 72.249149139679176, 78.497008365015603, 2740.4525568282425, 758.77464231590773, 0.82749656257702653, 1.2149653547407586, 2.8568561822109046, 1.4543742813123521, 2891.326563100044, 249700037.32349002, 6378.2727352121856, 14291.607313547367, 53432.723584443505, 229.25819910763389, 10502.745645037185, 99258.647168339405, 0.32428051011283876]\n"
     ]
    }
   ],
   "source": [
    "# model setup\n",
    "T = 50 # number of times to sample - 1(excluding P0)\n",
    "g = 0.85\n",
    "lamb = 1.15\n",
    "sig_0 = 0.015\n",
    "P_0 = price['close'][0] # initial price\n",
    "del_t = 1 # 1 day\n",
    "\n",
    "prices_sim, log_price_sim = simulate(T, sig_0, P_0, del_t)\n",
    "# price['prices_sim'] = prices_sim\n",
    "# price.plot(x='date',y = 'prices_sim')\n",
    "# print np.argwhere(np.isnan([prices_sim]))\n",
    "print prices_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sig_0 = np.std(price['log_ret'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014730360438136096"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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